Update app.py

app.py

@@ -96,6 +96,7 @@ def detect_fire(img):
         print(f"Error in fire detection: {e}")
         return False, 0.0
 
+
 def classify_severity(img):
     try:
         if xception_model is None or rf_model is None or xgb_model is None:
@@ -111,141 +112,115 @@ def classify_severity(img):
         print(f"Error in severity classification: {e}")
         return 'moderate'
 
+
 def fetch_weather_trend(lat, lon):
     try:
-        try:
-            end = datetime.utcnow()
-            start = end - timedelta(days=1)
-            url = API_URL.format(lat=lat, lon=lon, start=start.strftime('%Y-%m-%d'), end=end.strftime('%Y-%m-%d'))
-            response = requests.get(url, timeout=5)
-            if response.status_code != 200:
-                raise Exception(f"API returned status code {response.status_code}")
-            df = pd.DataFrame(response.json().get('daily', {}))
-        except Exception as e:
-            print(f"API error: {e}. Using synthetic data.")
-            df = pd.DataFrame({
-                'date': [(datetime.utcnow() - timedelta(days=i)).strftime('%Y-%m-%d') for i in range(1, -1, -1)],
-                'precipitation_sum': [5, 2],
-                'temperature_2m_max': [28, 30],
-                'temperature_2m_min': [18, 20],
-                'relative_humidity_2m_max': [70, 65],
-                'relative_humidity_2m_min': [40, 35],
-                'windspeed_10m_max': [15, 18]
-            })
-        for c in ['precipitation_sum','temperature_2m_max','temperature_2m_min',
-                'relative_humidity_2m_max','relative_humidity_2m_min','windspeed_10m_max']:
-            df[c] = pd.to_numeric(df.get(c,[]), errors='coerce')
-        df['precipitation'] = df['precipitation_sum'].fillna(0)
-        df['temperature'] = (df['temperature_2m_max'] + df['temperature_2m_min'])/2
-        df['humidity'] = (df['relative_humidity_2m_max'] + df['relative_humidity_2m_min'])/2
-        df['wind_speed'] = df['windspeed_10m_max']
-        df['fire_risk_score'] = (
-            0.4*(df['temperature']/55) +
-            0.2*(1-df['humidity']/100) +
-            0.3*(df['wind_speed']/60) +
-            0.1*(1-df['precipitation']/50)
+        end = datetime.utcnow()
+        start = end - timedelta(days=1)
+        url = API_URL.format(
+            lat=lat, lon=lon,
+            start=start.strftime('%Y-%m-%d'),
+            end=end.strftime('%Y-%m-%d')
         )
-        feats = df[['temperature','humidity','wind_speed','precipitation','fire_risk_score']]
-        feat = feats.fillna(feats.mean()).iloc[-1].values.reshape(1,-1)
-        if lr_model is not None:
-            trend_cl = lr_model.predict(feat)[0]
-            return trend_map.get(trend_cl, 'same')
-        else:
-            if df['fire_risk_score'].iloc[-1] > 0.6:
-                return 'increase'
-            elif df['fire_risk_score'].iloc[-1] < 0.4:
-                return 'decrease'
-            return 'same'
-    except Exception as e:
-        print(f"Error in weather trend analysis: {e}")
-        return 'same'
-
-def generate_recommendations(original_severity, weather_trend):
-    projected_severity = task_rules[original_severity][weather_trend]
-    rec = recommendations[projected_severity]
-    recommendation_text = f"""**Original Severity:** {original_severity.title()}  
-**Weather Trend:** {weather_trend.title()}  
-**Projected Severity:** {projected_severity.title()}
-
-### Management Recommendations:
-
-**1. Immediate Actions:**  
-{rec['immediate']}
-
-**2. Evacuation Guidelines:**  
-{rec['evacuation']}
-
-**3. Short-term Containment:**  
-{rec['containment']}
+        response = requests.get(url, timeout=5)
+        if response.status_code != 200:
+            raise Exception(f"API returned status {response.status_code}")
+        df = pd.DataFrame(response.json().get('daily', {}))
+    except Exception:
+        df = pd.DataFrame({
+            'date': [(datetime.utcnow() - timedelta(days=i)).strftime('%Y-%m-%d') for i in range(1,-1,-1)],
+            'precipitation_sum': [5, 2],
+            'temperature_2m_max': [28, 30],
+            'temperature_2m_min': [18, 20],
+            'relative_humidity_2m_max': [70, 65],
+            'relative_humidity_2m_min': [40, 35],
+            'windspeed_10m_max': [15, 18]
+        })
+    for c in ['precipitation_sum','temperature_2m_max','temperature_2m_min',
+              'relative_humidity_2m_max','relative_humidity_2m_min','windspeed_10m_max']:
+        df[c] = pd.to_numeric(df[c], errors='coerce')
+    df['precipitation'] = df['precipitation_sum']
+    df['temperature'] = (df['temperature_2m_max'] + df['temperature_2m_min'])/2
+    df['humidity'] = (df['relative_humidity_2m_max'] + df['relative_humidity_2m_min'])/2
+    df['wind_speed'] = df['windspeed_10m_max']
+    df['fire_risk_score'] = (
+        0.4*(df['temperature']/55) +
+        0.2*(1-df['humidity']/100) +
+        0.3*(df['wind_speed']/60) +
+        0.1*(1-df['precipitation']/50)
+    )
+    feat = df[['temperature','humidity','wind_speed','precipitation','fire_risk_score']].iloc[-1].values.reshape(1,-1)
+    if lr_model is not None:
+        trend_cl = lr_model.predict(feat)[0]
+        return trend_map.get(trend_cl,'same')
+    return 'same'
 
-**4. Long-term Prevention & Recovery:**  
-{rec['prevention']}
 
-**5. Community Education:**  
-{rec['education']}
-"""
-    return recommendation_text
+def generate_recommendations(original_severity, weather_trend):
+    projected = task_rules[original_severity][weather_trend]
+    rec = recommendations[projected]
+    return f"**Original Severity:** {original_severity.title()}  \n" \
+           f"**Weather Trend:** {weather_trend.title()}  \n" \
+           f"**Projected Severity:** {projected.title()}\n\n" \
+           "### Management Recommendations:\n" \
+           f"**Immediate:** {rec['immediate']}\n\n" \
+           f"**Evacuation:** {rec['evacuation']}\n\n" \
+           f"**Containment:** {rec['containment']}\n\n" \
+           f"**Prevention:** {rec['prevention']}\n\n" \
+           f"**Education:** {rec['education']}"
 
 # --- MAIN PIPELINE ---
 def pipeline(image):
     if image is None:
-        return "No image provided", "N/A", "N/A", "**Please upload an image to analyze**"
+        return "No image provided","N/A","N/A","**Please upload an image to analyze**"
     img = Image.fromarray(image).convert('RGB')
     fire, prob = detect_fire(img)
     if not fire:
-        return f"No wildfire detected (confidence: {(1-prob)*100:.1f}%)", "N/A", "N/A", "**No wildfire detected. Stay alert and maintain regular monitoring.**"
-    severity = classify_severity(img)
+        return (f"No wildfire detected (confidence: {(1-prob)*100:.1f}%)",
+                "N/A","N/A","**No wildfire detected. Stay alert.**")
+    sev = classify_severity(img)
     trend = fetch_weather_trend(*FOREST_COORDS['Pakistan Forest'])
-    recommendations_text = generate_recommendations(severity, trend)
-    return f"Wildfire detected (confidence: {prob*100:.1f}%)", severity.title(), trend.title(), recommendations_text
+    recs = generate_recommendations(sev, trend)
+    return (f"**Wildfire detected** (confidence: {prob*100:.1f}%)",
+            f"**{sev.title()}**",
+            f"**{trend.title()}**",
+            recs)
 
 # --- LOAD MODELS GLOBALLY ---
 vgg_model, xception_model, rf_model, xgb_model, lr_model = load_models()
 
-# --- GRADIO BLOCKS UI WITH ENHANCED TEXT & STYLING ---
+# --- GRADIO BLOCKS UI & STYLING ---
 custom_css = """
-.sidebar { background: #1f2937; color: #f9fafb; padding: 1rem; border-radius: 1rem; height: 100%; }
-#main-title { font-size: 2.75rem; font-weight: 700; color: #111827; margin-bottom: 0.25em; }
-#sub-title { font-size: 1.125rem; color: #4b5563; margin-bottom: 1.5em; }
-.card { background: #ffffff; border-radius: 1rem; box-shadow: 0 4px 16px rgba(0,0,0,0.08); padding: 1rem; margin-bottom: 1rem; }
-.gr-button { border-radius: 0.75rem; padding: 0.75rem 1.5rem; font-size: 1rem; }
-.status-badge { display: inline-block; padding: 0.25em 0.75em; border-radius: 9999px; font-weight: 600; margin-bottom: 0.5em; }
-.status-fire { background: #dc2626; color: white; }
-.status-no-fire { background: #16a34a; color: white; }
+.sidebar { background: #2e3440; color: #eceff4; padding: 1rem; border-radius: 1rem; }
+#main-title { font-size: 2.5rem; color: #3b4252; }
+#sub-title { font-size: 1.125rem; color: #4c566a; }
+.card { background: #eceff4; color: #2e3440; border-radius: 0.75rem; padding: 1rem; margin-bottom: 1rem; box-shadow: 0 2px 8px rgba(0,0,0,0.1); }
+.gr-button { background: #5e81ac !important; color: white !important; border-radius: 0.5rem; }
+.status-badge { padding: 0.25em 0.75em; border-radius: 9999px; font-weight: 600; }
+.status-fire { background: #bf616a; color: white; }
+.status-no-fire { background: #a3be8c; color: white; }
+.gr-markdown { color: #2e3440; }
 """
 
 with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
     with gr.Row():
         with gr.Column(scale=2):
-            gr.Markdown("🔥 **Wildfire Command Center**", elem_id="main-title")
+            gr.Markdown("# 🔥 Wildfire Command Center", elem_id="main-title")
             gr.Markdown(
-                "Upload a **forest image** from Pakistan to automatically detect wildfire, "
-                "classify its severity, fetch the latest weather-driven risk trend, "
-                "and receive expert management recommendations.",
+                "Upload a **forest image** to detect wildfire, classify severity, fetch weather trend, and get management recommendations.",
                 elem_id="sub-title"
             )
-            image_input = gr.Image(type="numpy", label="Select Forest Image")
-            run_btn = gr.Button("Analyze Image", variant="primary")
+            image_input = gr.Image(type="numpy", label="Upload Forest Image")
+            run_btn = gr.Button("Analyze Now", variant="primary")
         with gr.Column(scale=1, elem_classes="sidebar"):
-            gr.Markdown("### 📊 Last Analysis")
-            last_status = gr.HTML("<div class='card'>No run yet</div>")
-            last_severity = gr.HTML("<div class='card'>–</div>")
-            last_trend = gr.HTML("<div class='card'>–</div>")
-            last_recs = gr.HTML("<div class='card'><i>Recommendations will appear here</i></div>")
-
-    def run_and_update(image):
-        status, sev, trend, recs = pipeline(image)
-        badge_class = "status-fire" if "Wildfire detected" in status else "status-no-fire"
-        status_html = f"<div class='card'><span class='status-badge {badge_class}'>{status}</span></div>"
-        return (
-            status_html,
-            f"<div class='card'><b>{sev}</b></div>",
-            f"<div class='card'><b>{trend}</b></div>",
-            f"<div class='card'>{recs}</div>"
-        )
+            gr.Markdown("## 📊 Last Analysis")
+            last_status = gr.Markdown("*No analysis yet*")
+            last_severity = gr.Markdown("---")
+            last_trend = gr.Markdown("---")
+            last_recs = gr.Markdown("---")
 
     run_btn.click(
-        fn=run_and_update,
+        fn=pipeline,
         inputs=image_input,
         outputs=[last_status, last_severity, last_trend, last_recs]
     )